437 lines
16 KiB
Python
437 lines
16 KiB
Python
from functools import partial
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from typing import Any, Callable, List, Optional
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import torch
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from torch import nn, Tensor
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from torchvision.transforms._presets import ImageClassification
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from torchvision.utils import _log_api_usage_once
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from torchvision.models._api import Weights, WeightsEnum
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from torchvision.models._meta import _IMAGENET_CATEGORIES
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from torchvision.models._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface
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import warnings
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from typing import Callable, List, Optional, Sequence, Tuple, Union, TypeVar
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import collections
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from itertools import repeat
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M = TypeVar("M", bound=nn.Module)
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BUILTIN_MODELS = {}
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def register_model(name: Optional[str] = None) -> Callable[[Callable[..., M]], Callable[..., M]]:
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def wrapper(fn: Callable[..., M]) -> Callable[..., M]:
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key = name if name is not None else fn.__name__
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if key in BUILTIN_MODELS:
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raise ValueError(f"An entry is already registered under the name '{key}'.")
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BUILTIN_MODELS[key] = fn
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return fn
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return wrapper
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def _make_ntuple(x: Any, n: int) -> Tuple[Any, ...]:
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"""
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Make n-tuple from input x. If x is an iterable, then we just convert it to tuple.
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Otherwise, we will make a tuple of length n, all with value of x.
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reference: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/utils.py#L8
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Args:
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x (Any): input value
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n (int): length of the resulting tuple
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"""
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if isinstance(x, collections.abc.Iterable):
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return tuple(x)
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return tuple(repeat(x, n))
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class ConvNormActivation(torch.nn.Sequential):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, ...]] = 3,
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stride: Union[int, Tuple[int, ...]] = 1,
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padding: Optional[Union[int, Tuple[int, ...], str]] = None,
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groups: int = 1,
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norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
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activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
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dilation: Union[int, Tuple[int, ...]] = 1,
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inplace: Optional[bool] = True,
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bias: Optional[bool] = None,
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conv_layer: Callable[..., torch.nn.Module] = torch.nn.Conv2d,
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) -> None:
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if padding is None:
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if isinstance(kernel_size, int) and isinstance(dilation, int):
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padding = (kernel_size - 1) // 2 * dilation
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else:
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_conv_dim = len(kernel_size) if isinstance(kernel_size, Sequence) else len(dilation)
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kernel_size = _make_ntuple(kernel_size, _conv_dim)
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dilation = _make_ntuple(dilation, _conv_dim)
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padding = tuple((kernel_size[i] - 1) // 2 * dilation[i] for i in range(_conv_dim))
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if bias is None:
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bias = norm_layer is None
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layers = [
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conv_layer(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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)
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]
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if norm_layer is not None:
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layers.append(norm_layer(out_channels))
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if activation_layer is not None:
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params = {} if inplace is None else {"inplace": inplace}
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layers.append(activation_layer(**params))
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super().__init__(*layers)
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_log_api_usage_once(self)
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self.out_channels = out_channels
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if self.__class__ == ConvNormActivation:
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warnings.warn(
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"Don't use ConvNormActivation directly, please use Conv2dNormActivation and Conv3dNormActivation instead."
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)
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class Conv2dNormActivation(ConvNormActivation):
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"""
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Configurable block used for Convolution2d-Normalization-Activation blocks.
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Args:
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in_channels (int): Number of channels in the input image
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out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block
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kernel_size: (int, optional): Size of the convolving kernel. Default: 3
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stride (int, optional): Stride of the convolution. Default: 1
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padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation``
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groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
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norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm2d``
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activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU``
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dilation (int): Spacing between kernel elements. Default: 1
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inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
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bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, int]] = 3,
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stride: Union[int, Tuple[int, int]] = 1,
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padding: Optional[Union[int, Tuple[int, int], str]] = None,
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groups: int = 1,
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norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
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activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
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dilation: Union[int, Tuple[int, int]] = 1,
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inplace: Optional[bool] = True,
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bias: Optional[bool] = None,
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) -> None:
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super().__init__(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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groups,
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norm_layer,
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activation_layer,
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dilation,
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inplace,
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bias,
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torch.nn.Conv2d,
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)
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__all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]
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# necessary for backwards compatibility
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class InvertedResidual(nn.Module):
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def __init__(
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self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super().__init__()
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self.stride = stride
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if stride not in [1, 2]:
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raise ValueError(f"stride should be 1 or 2 instead of {stride}")
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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hidden_dim = int(round(inp * expand_ratio))
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self.use_res_connect = self.stride == 1 and inp == oup
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layers: List[nn.Module] = []
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if expand_ratio != 1:
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# pw
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layers.append(
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Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
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)
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layers.extend(
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[
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# dw
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Conv2dNormActivation(
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hidden_dim,
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hidden_dim,
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stride=stride,
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groups=hidden_dim,
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norm_layer=norm_layer,
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activation_layer=nn.ReLU6,
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),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
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norm_layer(oup),
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]
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)
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self.conv = nn.Sequential(*layers)
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self.out_channels = oup
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self._is_cn = stride > 1
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def forward(self, x: Tensor) -> Tensor:
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if self.use_res_connect:
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return x + self.conv(x)
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else:
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return self.conv(x)
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class MobileNetV2(nn.Module):
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def __init__(
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self,
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num_classes: int = 1000,
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width_mult: float = 1.0,
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inverted_residual_setting: Optional[List[List[int]]] = None,
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round_nearest: int = 8,
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block: Optional[Callable[..., nn.Module]] = None,
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norm_layer: Optional[Callable[..., nn.Module]] = None,
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dropout: float = 0.2,
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) -> None:
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"""
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MobileNet V2 main class
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Args:
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num_classes (int): Number of classes
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width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
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inverted_residual_setting: Network structure
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round_nearest (int): Round the number of channels in each layer to be a multiple of this number
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Set to 1 to turn off rounding
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block: Module specifying inverted residual building block for mobilenet
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norm_layer: Module specifying the normalization layer to use
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dropout (float): The droupout probability
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"""
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super().__init__()
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_log_api_usage_once(self)
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if block is None:
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block = InvertedResidual
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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input_channel = 32
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last_channel = 1280
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if inverted_residual_setting is None:
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inverted_residual_setting = [
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# t, c, n, s
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[1, 16, 1, 1],
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[6, 24, 2, 1],
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[6, 32, 3, 1],
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[6, 64, 4, 2],
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[6, 96, 3, 1],
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[6, 160, 3, 2],
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[6, 320, 1, 1],
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]
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# only check the first element, assuming user knows t,c,n,s are required
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if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
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raise ValueError(
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f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}"
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)
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# building first layer
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input_channel = _make_divisible(input_channel * width_mult, round_nearest)
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self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
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features: List[nn.Module] = [
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Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
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]
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# building inverted residual blocks
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for t, c, n, s in inverted_residual_setting:
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output_channel = _make_divisible(c * width_mult, round_nearest)
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for i in range(n):
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stride = s if i == 0 else 1
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features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
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input_channel = output_channel
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# building last several layers
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features.append(
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Conv2dNormActivation(
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input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6
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)
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)
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# make it nn.Sequential
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self.features = nn.Sequential(*features)
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# self.layer1 = nn.Sequential(*features[:])
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# self.layer2 = features[57:120]
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# self.layer3 = features[120:]
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# building classifier
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self.classifier = nn.Sequential(
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nn.Dropout(p=dropout),
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nn.Linear(self.last_channel, num_classes),
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)
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# weight initialization
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out")
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.ones_(m.weight)
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.zeros_(m.bias)
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def _forward_impl(self, x: Tensor) -> Tensor:
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# This exists since TorchScript doesn't support inheritance, so the superclass method
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# (this one) needs to have a name other than `forward` that can be accessed in a subclass
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out_layers = []
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for layer in self.features.named_modules():
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for i, layer1 in enumerate(layer[1]):
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# print(layer1)
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x = layer1(x)
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# print("第{}层，输出大小{}".format(i, x.shape))
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if i in [0, 10, 18]:
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out_layers.append(x)
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break
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# x = self.features(x)
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# Cannot use "squeeze" as batch-size can be 1
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# x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
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# x = torch.flatten(x, 1)
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# x = self.classifier(x)
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return out_layers
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def forward(self, x: Tensor) -> Tensor:
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return self._forward_impl(x)
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_COMMON_META = {
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"num_params": 3504872,
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"min_size": (1, 1),
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"categories": _IMAGENET_CATEGORIES,
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}
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class MobileNet_V2_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
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transforms=partial(ImageClassification, crop_size=224),
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meta={
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**_COMMON_META,
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 71.878,
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"acc@5": 90.286,
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}
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},
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"_ops": 0.301,
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"_file_size": 13.555,
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"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
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},
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)
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IMAGENET1K_V2 = Weights(
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url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth",
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transforms=partial(ImageClassification, crop_size=224, resize_size=232),
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meta={
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**_COMMON_META,
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"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 72.154,
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"acc@5": 90.822,
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}
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},
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"_ops": 0.301,
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"_file_size": 13.598,
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"_docs": """
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These weights improve upon the results of the original paper by using a modified version of TorchVision's
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`new training recipe
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<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
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""",
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},
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)
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DEFAULT = IMAGENET1K_V2
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# @register_model()
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# @handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.IMAGENET1K_V1))
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def mobilenet_v2(
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*, weights: Optional[MobileNet_V2_Weights] = MobileNet_V2_Weights.IMAGENET1K_V1, progress: bool = True, **kwargs: Any
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) -> MobileNetV2:
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"""MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear
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Bottlenecks <https://arxiv.org/abs/1801.04381>`_ paper.
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Args:
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weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.MobileNet_V2_Weights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the
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download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.mobilenetv2.MobileNetV2``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv2.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.MobileNet_V2_Weights
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:members:
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"""
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weights = MobileNet_V2_Weights.verify(weights)
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if weights is not None:
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
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model = MobileNetV2(**kwargs)
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if weights is not None:
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model.load_state_dict(weights.get_state_dict(progress=progress))
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return model
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class MobileNetv2Wrapper(nn.Module):
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def __init__(self):
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super(MobileNetv2Wrapper, self).__init__()
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weights = MobileNet_V2_Weights.verify(MobileNet_V2_Weights.IMAGENET1K_V1)
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self.model = MobileNetV2()
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if weights is not None:
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self.model.load_state_dict(weights.get_state_dict(progress=True))
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self.out3 = conv1x1(1280, 128)
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def forward(self, x):
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# print(x.shape)
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out_layers = self.model(x)
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# print(x.shape)
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# out_layers[0] = self.out1(out_layers[0])
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# out_layers[1] = self.out2(out_layers[1])
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out_layers[2] = self.out3(out_layers[2])
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# print(x.shape)
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return out_layers
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